ragflow/api/db/services/llm_service.py

#
#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#
import json
import logging
import os

from api.db.services.user_service import TenantService
from api.utils.file_utils import get_project_base_directory
from rag.llm import EmbeddingModel, CvModel, ChatModel, RerankModel, Seq2txtModel, TTSModel
from api.db import LLMType
from api.db.db_models import DB
from api.db.db_models import LLMFactories, LLM, TenantLLM
from api.db.services.common_service import CommonService


class LLMFactoriesService(CommonService):
    model = LLMFactories


class LLMService(CommonService):
    model = LLM


class TenantLLMService(CommonService):
    model = TenantLLM

    @classmethod
    @DB.connection_context()
    def get_api_key(cls, tenant_id, model_name):
        mdlnm, fid = TenantLLMService.split_model_name_and_factory(model_name)
        if not fid:
            objs = cls.query(tenant_id=tenant_id, llm_name=mdlnm)
        else:
            objs = cls.query(tenant_id=tenant_id, llm_name=mdlnm, llm_factory=fid)
        if not objs:
            return
        return objs[0]

    @classmethod
    @DB.connection_context()
    def get_my_llms(cls, tenant_id):
        fields = [
            cls.model.llm_factory,
            LLMFactories.logo,
            LLMFactories.tags,
            cls.model.model_type,
            cls.model.llm_name,
            cls.model.used_tokens
        ]
        objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(
            cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()

        return list(objs)

    @staticmethod
    def split_model_name_and_factory(model_name):
        arr = model_name.split("@")
        if len(arr) < 2:
            return model_name, None
        if len(arr) > 2:
            return "@".join(arr[0:-1]), arr[-1]

        # model name must be xxx@yyy
        try:
            model_factories = json.load(open(os.path.join(get_project_base_directory(), "conf/llm_factories.json"), "r"))["factory_llm_infos"]
            model_providers = set([f["name"] for f in model_factories])
            if arr[-1] not in model_providers:
                return model_name, None
            return arr[0], arr[-1]
        except Exception as e:
            logging.exception(f"TenantLLMService.split_model_name_and_factory got exception: {e}")
        return model_name, None

    @classmethod
    @DB.connection_context()
    def model_instance(cls, tenant_id, llm_type,
                       llm_name=None, lang="Chinese"):
        e, tenant = TenantService.get_by_id(tenant_id)
        if not e:
            raise LookupError("Tenant not found")

        if llm_type == LLMType.EMBEDDING.value:
            mdlnm = tenant.embd_id if not llm_name else llm_name
        elif llm_type == LLMType.SPEECH2TEXT.value:
            mdlnm = tenant.asr_id
        elif llm_type == LLMType.IMAGE2TEXT.value:
            mdlnm = tenant.img2txt_id if not llm_name else llm_name
        elif llm_type == LLMType.CHAT.value:
            mdlnm = tenant.llm_id if not llm_name else llm_name
        elif llm_type == LLMType.RERANK:
            mdlnm = tenant.rerank_id if not llm_name else llm_name
        elif llm_type == LLMType.TTS:
            mdlnm = tenant.tts_id if not llm_name else llm_name
        else:
            assert False, "LLM type error"

        model_config = cls.get_api_key(tenant_id, mdlnm)
        mdlnm, fid = TenantLLMService.split_model_name_and_factory(mdlnm)
        if model_config:
            model_config = model_config.to_dict()
        if not model_config:
            if llm_type in [LLMType.EMBEDDING, LLMType.RERANK]:
                llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid)
                if llm and llm[0].fid in ["Youdao", "FastEmbed", "BAAI"]:
                    model_config = {"llm_factory": llm[0].fid, "api_key": "", "llm_name": mdlnm, "api_base": ""}
            if not model_config:
                if mdlnm == "flag-embedding":
                    model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "",
                                    "llm_name": llm_name, "api_base": ""}
                else:
                    if not mdlnm:
                        raise LookupError(f"Type of {llm_type} model is not set.")
                    raise LookupError("Model({}) not authorized".format(mdlnm))

        if llm_type == LLMType.EMBEDDING.value:
            if model_config["llm_factory"] not in EmbeddingModel:
                return
            return EmbeddingModel[model_config["llm_factory"]](
                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])

        if llm_type == LLMType.RERANK:
            if model_config["llm_factory"] not in RerankModel:
                return
            return RerankModel[model_config["llm_factory"]](
                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])

        if llm_type == LLMType.IMAGE2TEXT.value:
            if model_config["llm_factory"] not in CvModel:
                return
            return CvModel[model_config["llm_factory"]](
                model_config["api_key"], model_config["llm_name"], lang,
                base_url=model_config["api_base"]
            )

        if llm_type == LLMType.CHAT.value:
            if model_config["llm_factory"] not in ChatModel:
                return
            return ChatModel[model_config["llm_factory"]](
                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])

        if llm_type == LLMType.SPEECH2TEXT:
            if model_config["llm_factory"] not in Seq2txtModel:
                return
            return Seq2txtModel[model_config["llm_factory"]](
                key=model_config["api_key"], model_name=model_config["llm_name"],
                lang=lang,
                base_url=model_config["api_base"]
            )
        if llm_type == LLMType.TTS:
            if model_config["llm_factory"] not in TTSModel:
                return
            return TTSModel[model_config["llm_factory"]](
                model_config["api_key"],
                model_config["llm_name"],
                base_url=model_config["api_base"],
            )

    @classmethod
    @DB.connection_context()
    def increase_usage(cls, tenant_id, llm_type, used_tokens, llm_name=None):
        e, tenant = TenantService.get_by_id(tenant_id)
        if not e:
            logging.error(f"Tenant not found: {tenant_id}")
            return 0

        llm_map = {
            LLMType.EMBEDDING.value: tenant.embd_id,
            LLMType.SPEECH2TEXT.value: tenant.asr_id,
            LLMType.IMAGE2TEXT.value: tenant.img2txt_id,
            LLMType.CHAT.value: tenant.llm_id if not llm_name else llm_name,
            LLMType.RERANK.value: tenant.rerank_id if not llm_name else llm_name,
            LLMType.TTS.value: tenant.tts_id if not llm_name else llm_name
        }

        mdlnm = llm_map.get(llm_type)
        if mdlnm is None:
            logging.error(f"LLM type error: {llm_type}")
            return 0

        llm_name, llm_factory = TenantLLMService.split_model_name_and_factory(mdlnm)

        try:
            num = cls.model.update(
                used_tokens=cls.model.used_tokens + used_tokens
            ).where(
                cls.model.tenant_id == tenant_id,
                cls.model.llm_name == llm_name,
                cls.model.llm_factory == llm_factory if llm_factory else True
            ).execute()
        except Exception:
            logging.exception(
                "TenantLLMService.increase_usage got exception,Failed to update used_tokens for tenant_id=%s, llm_name=%s",
                tenant_id, llm_name)
            return 0

        return num

    @classmethod
    @DB.connection_context()
    def get_openai_models(cls):
        objs = cls.model.select().where(
            (cls.model.llm_factory == "OpenAI"),
            ~(cls.model.llm_name == "text-embedding-3-small"),
            ~(cls.model.llm_name == "text-embedding-3-large")
        ).dicts()
        return list(objs)


class LLMBundle(object):
    def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese"):
        self.tenant_id = tenant_id
        self.llm_type = llm_type
        self.llm_name = llm_name
        self.mdl = TenantLLMService.model_instance(
            tenant_id, llm_type, llm_name, lang=lang)
        assert self.mdl, "Can't find model for {}/{}/{}".format(
            tenant_id, llm_type, llm_name)
        self.max_length = 8192
        for lm in LLMService.query(llm_name=llm_name):
            self.max_length = lm.max_tokens
            break

    def encode(self, texts: list):
        embeddings, used_tokens = self.mdl.encode(texts)
        if not TenantLLMService.increase_usage(
                self.tenant_id, self.llm_type, used_tokens):
            logging.error(
                "LLMBundle.encode can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
        return embeddings, used_tokens

    def encode_queries(self, query: str):
        emd, used_tokens = self.mdl.encode_queries(query)
        if not TenantLLMService.increase_usage(
                self.tenant_id, self.llm_type, used_tokens):
            logging.error(
                "LLMBundle.encode_queries can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
        return emd, used_tokens

    def similarity(self, query: str, texts: list):
        sim, used_tokens = self.mdl.similarity(query, texts)
        if not TenantLLMService.increase_usage(
                self.tenant_id, self.llm_type, used_tokens):
            logging.error(
                "LLMBundle.similarity can't update token usage for {}/RERANK used_tokens: {}".format(self.tenant_id, used_tokens))
        return sim, used_tokens

    def describe(self, image, max_tokens=300):
        txt, used_tokens = self.mdl.describe(image, max_tokens)
        if not TenantLLMService.increase_usage(
                self.tenant_id, self.llm_type, used_tokens):
            logging.error(
                "LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens))
        return txt

    def transcription(self, audio):
        txt, used_tokens = self.mdl.transcription(audio)
        if not TenantLLMService.increase_usage(
                self.tenant_id, self.llm_type, used_tokens):
            logging.error(
                "LLMBundle.transcription can't update token usage for {}/SEQUENCE2TXT used_tokens: {}".format(self.tenant_id, used_tokens))
        return txt

    def tts(self, text):
        for chunk in self.mdl.tts(text):
            if isinstance(chunk, int):
                if not TenantLLMService.increase_usage(
                        self.tenant_id, self.llm_type, chunk, self.llm_name):
                    logging.error(
                        "LLMBundle.tts can't update token usage for {}/TTS".format(self.tenant_id))
                return
            yield chunk

    def chat(self, system, history, gen_conf):
        txt, used_tokens = self.mdl.chat(system, history, gen_conf)
        if isinstance(txt, int) and not TenantLLMService.increase_usage(
                self.tenant_id, self.llm_type, used_tokens, self.llm_name):
            logging.error(
                "LLMBundle.chat can't update token usage for {}/CHAT llm_name: {}, used_tokens: {}".format(self.tenant_id, self.llm_name,
                                                                                                           used_tokens))
        return txt

    def chat_streamly(self, system, history, gen_conf):
        for txt in self.mdl.chat_streamly(system, history, gen_conf):
            if isinstance(txt, int):
                if not TenantLLMService.increase_usage(
                        self.tenant_id, self.llm_type, txt, self.llm_name):
                    logging.error(
                        "LLMBundle.chat_streamly can't update token usage for {}/CHAT llm_name: {}, content: {}".format(self.tenant_id, self.llm_name,
                                                                                                                        txt))
                return
            yield txt